Jaroslav Křivánek

Efficient Caustic Rendering with Lightweight Photon Mapping

Pascal Grittmann Saarland Univesity

Arsene Pérard-Gayot Saarland Univesity

Philipp Slusallek DFKI Saarland Univesity

Jaroslav Křivánek Charles University, Prague

Different photon emission strategies in the CAR scene. We achieve a significantly better photon density inside caustics with fewer photons. The emission directions and the number of light paths (and thus photons) are optimized automatically by our method.

Abstract

Robust and efficient rendering of complex lighting effects, such as caustics, remains a challenging task. While algorithms like vertex connection and merging can render such effects robustly, their significant overhead over a simple path tracer is not always justified and – as we show in this paper – also not necessary. In current rendering solutions, caustics often require the user to enable a specialized algorithm, usually a photon mapper, and hand-tune its parameters. But even with carefully chosen parameters, photon mapping may still trace many photons that the path tracer could sample well enough, or, even worse, that are not visible at all.
Our goal is robust, yet lightweight, caustics rendering. To that end, we propose a technique to identify and focus computation on the photon paths that offer significant variance reduction over samples from a path tracer.We apply this technique in a rendering solution combining path tracing and photon mapping. The photon emission is automatically guided towards regions where the photons are useful, i.e., provide substantial variance reduction for the currently rendered image. Our method achieves better photon densities with fewer light paths (and thus photons) than emission guiding approaches based on visual importance. In addition, we automatically determine an appropriate number of photons for a given

Reference

Pascal Grittmann, Arsene Pérard-Gayot, Philipp Slusallek, and Jaroslav Křivánek. Efficient Caustic Rendering with Lightweight Photon Mapping. Computer Graphics Forum (Proceedings of the 29th Eurographics Symposium on Rendering), 37(4): 133-142, 2018
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Acknowledgments

This work was supported by the German Research Foundation (DFG): SFB 1233, Robust Vision: Inference Principles and Neural Mechanisms, TP 2. It received further funding from the European Union’s Horizon 2020 research and innovation program, under the Marie Skłodowska-Curie grant agreement No 642841 (DISTRO) and was supported by the Czech Science Foundation grant 16-18964S and the Charles University grant SVV-2017-260452.